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Title: Incremental k-core decomposition: Algorithms and evaluation

Journal Article · · The VLDB Journal
 [1];  [2];  [3];  [3];  [4]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Bilkent Univ., Ankara (Turkey)
  3. IBM T.J. Watson Research Center, Yorktown Heights, NY (United States)
  4. The Ohio State Univ., Columbus, OH (United States)
+ Show Author Affiliations

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1239351
Report Number(s):
SAND2016-1193J; 619260
Journal Information:
The VLDB Journal, Vol. 9, Issue 10; ISSN 1066-8888
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 44 works
Citation information provided by
Web of Science

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Cited By (8)

Core Decomposition of Massive, Information-Rich Graphs book January 2018
Effective and efficient attributed community search journal September 2017
A survey of community search over big graphs journal July 2019
The core decomposition of networks: theory, algorithms and applications journal November 2019
Coreness Variation Rule and Fast Updating Algorithm for Dynamic Networks journal April 2019
Core Decomposition of Massive, Information-Rich Graphs book January 2017
A Survey of Community Search Over Big Graphs preprint January 2019
Recent Advances in Fully Dynamic Graph Algorithms (Invited Talk) text January 2022

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97 MATHEMATICS AND COMPUTING